Mambu is customer relationship software for banking and lending businesses. It lets these businesses launch a digitally native financial services platform.

Apache Superset is a cloud-native data exploration and visualization platform that businesses can use to create business intelligence reports and dashboards. It includes a state-of-the-art SQL IDE, and it's open source software, free of cost. The platform was originally developed at Airbnb and donated to the Apache Software Foundation.

Mambu provides a RESTful API that lets developers retrieve data stored in the platform about all kinds of financial objects and entities. For example, to retrieve information about a specific deposit account, you would call GET /deposits/{depositAccountId}.

Here's an example of the kind of response you might see with a query like the one above.

{
  "accountState": "PENDING_APPROVAL",
  "migrationEventKey": "string",
  "notes": "string",
  "lastSetToArrearsDate": "2019-09-06T13:37:50+03:00",
  "assignedBranchKey": "string",
  "lastOverdraftInterestReviewDate": "2019-09-06T13:37:50+03:00",
  "lastInterestStoredDate": "2019-09-06T13:37:50+03:00",
  "interestSettings": {
    "interestRateSettings": {
      "interestRate": 0,
      "interestRateTiers": [
        {
          "endingBalance": 0,
          "interestRate": 0,
          "encodedKey": "string",
          "endingDay": 0
        }
      ],
      "interestChargeFrequency": "ANNUALIZED",
      "encodedKey": "string",
      "interestChargeFrequencyCount": 0,
      "interestRateTerms": "FIXED"
    },
    "interestPaymentSettings": {
      "interestPaymentDates": [
        {
          "month": 0,
          "day": 0
        }
      ],
      "interestPaymentPoint": "FIRST_DAY_OF_MONTH"
    }
  },
  "balances": {
    "overdraftInterestDue": 0,
    "totalBalance": 0,
    "lockedBalance": 0,
    "technicalOverdraftAmount": 0,
    "overdraftAmount": 0,
    "holdBalance": 0,
    "technicalOverdraftInterestDue": 0,
    "feesDue": 0,
    "availableBalance": 0
  },
  "creditArrangementKey": "string",
  "maturityDate": "2019-09-06T13:37:50+03:00",
  "encodedKey": "string",
  "id": "string",
  "overdraftSettings": {
    "allowOverdraft": true,
    "overdraftLimit": 0,
    "overdraftExpiryDate": "2019-09-06T13:37:50+03:00"
  },
  "lastAccountAppraisalDate": "2019-09-06T13:37:50+03:00",
  "withholdingTaxSourceKey": "string",
  "assignedUserKey": "string",
  "overdraftInterestSettings": {
    "interestRateSettings": {
      "interestRate": 0,
      "interestSpread": 0,
      "interestRateReviewUnit": "DAYS",
      "interestRateSource": "FIXED_INTEREST_RATE",
      "interestRateReviewCount": 0,
      "interestRateTiers": [
        {
          "endingBalance": 0,
          "interestRate": 0,
          "encodedKey": "string",
          "endingDay": 0
        }
      ],
      "interestChargeFrequency": "ANNUALIZED",
      "encodedKey": "string",
      "interestChargeFrequencyCount": 0,
      "interestRateTerms": "FIXED"
    }
  },
  "lastModifiedDate": "2019-09-06T13:37:50+03:00",
  "accountType": "CURRENT_ACCOUNT",
  "lockedDate": "2019-09-06T13:37:50+03:00",
  "creationDate": "2019-09-06T13:37:50+03:00",
  "lastInterestCalculationDate": "2019-09-06T13:37:50+03:00",
  "assignedCentreKey": "string",
  "approvedDate": "2019-09-06T13:37:50+03:00",
  "closedDate": "2019-09-06T13:37:50+03:00",
  "accruedAmounts": {
    "overdraftInterestAccrued": 0,
    "interestAccrued": 0,
    "technicalOverdraftInterestAccrued": 0
  },
  "name": "string",
  "accountHolderKey": "string",
  "productTypeKey": "string",
  "activationDate": "2019-09-06T13:37:50+03:00",
  "internalControls": {
    "recommendedDepositAmount": 0,
    "targetAmount": 0,
    "maxWithdrawalAmount": 0
  },
  "currencyCode": "string",
  "accountHolderType": "CLIENT",
  "linkedSettlementAccountKeys": [
    "string"
  ]
}

If you don't already have a data structure in which to store the data you retrieve, you'll have to create a schema for your data tables. Then, for each value in the response, you'll need to identify a predefined datatype (INTEGER, DATETIME, etc.) and build a table that can receive them. The Mambu documentation should tell you what fields are provided by each endpoint, along with their corresponding datatypes.

Complicating things is the fact that the records retrieved from the source may not always be "flat" – some of the objects may actually be lists. In these cases you'll likely have to create additional tables to capture the unpredictable cardinality in each record.

You must replicate data from your SaaS applications to a data warehouse before you can report on it using Superset. Superset can connect to almost 30 databases and data warehouses. Once you choose a data source you want to connect to, you must specify a host name and port, database name, and username and password to get access to the data. You then specify the database schema or tables you want to work with.

At this point you've coded up a script or written a program to get the data you want and successfully moved it into your data warehouse. But how will you load new or updated data? It's not a good idea to replicate all of your data each time you have updated records. That process would be painfully slow and resource-intensive.

The key is to build your script in such a way that it can identify incremental updates to your data. Thankfully, Mambu's API results include fields like activationDate and lastModifiedDate that allow you to identify records that are new since your last update (or since the newest record you've copied). Once you've taken new data into account, you can set your script up as a cron job or continuous loop to keep pulling down new data as it appears.